1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
5/5/2024 Agua 16101 Andrés PAC AGUAS ANDIN 000000005687837
11/5/2024 Comida 72079 Tami Supermercado
13/5/2024 Comida 28480 Andrés piwen
13/5/2024 Enceres 40000 Andrés mantencion toyotomi
17/5/2024 Comida 59132 Tami Supermercado
18/5/2024 Gas 94000 Andrés 2 lks mas
24/5/2024 Comida 22384 Tami Barritas wild soul
26/5/2024 Comida 53958 Tami Supermercado
28/5/2024 cartero 4000 Andrés NA
29/5/2024 Electricidad 35206 Andrés NA
1/6/2024 Comida 68402 Tami NA
1/6/2024 Comida 11560 Andrés lider gastos
1/6/2024 Comida 16500 Andrés teteria
2/6/2024 Parafina 39138 Tami NA
4/6/2024 Comida 6780 Andrés avena
9/6/2024 Comida 67000 Tami Supermercado
15/6/2024 Gas 17550 Andrés NA
15/6/2024 Comida 6530 Tami Cus cus y orégano
17/6/2024 VTR 21990 Andrés NA
19/6/2024 Diosi 18180 Tami Pelet y pastita Diosi
22/6/2024 VTR 22000 Andrés entel
23/6/2024 Comida 70795 Tami Supermercado
25/6/2024 Enceres 11790 Tami Confort 40 rollos
28/6/2024 Diosi 16002 Andrés pipeta
29/6/2024 Comida 65070 Tami Supermercado
29/6/2024 Comida 6400 Andrés lider
29/6/2024 Electricidad 55385 Andrés pac enel
29/6/2024 Enceres 42000 Andrés ida al sodimac
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 8.0464e+08   2    7.2318  8e-04 ***
## lag_depvar    1.2187e+11   1 2190.6877 <2e-16 ***
## Residuals     4.0277e+10 724                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr     p adj
## 1-0  7228.838   554.5366 13903.14 0.0300234
## 2-0 29635.319 23600.1708 35670.47 0.0000000
## 2-1 22406.481 18879.9326 25933.03 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
## 30   28331.57             0   28706.00
## 31   25617.86             0   28331.57
## 32   27223.29             0   25617.86
## 33   31622.57             0   27223.29
## 34   32021.43             0   31622.57
## 35   33634.57             0   32021.43
## 36   30784.86             0   33634.57
## 37   34770.57             0   30784.86
## 38   38443.00             1   34770.57
## 39   35073.00             1   38443.00
## 40   31422.29             1   35073.00
## 41   30103.29             1   31422.29
## 42   19319.29             1   30103.29
## 43   27926.29             1   19319.29
## 44   30715.43             1   27926.29
## 45   31962.29             1   30715.43
## 46   39790.14             1   31962.29
## 47   39211.57             1   39790.14
## 48   44548.57             1   39211.57
## 49   49398.00             1   44548.57
## 50   41039.00             1   49398.00
## 51   34821.29             1   41039.00
## 52   29123.57             1   34821.29
## 53   21275.71             1   29123.57
## 54   28476.14             1   21275.71
## 55   24561.86             1   28476.14
## 56   20323.57             1   24561.86
## 57   25370.00             1   20323.57
## 58   26811.86             1   25370.00
## 59   27151.86             1   26811.86
## 60   27623.29             1   27151.86
## 61   22896.57             1   27623.29
## 62   41889.29             1   22896.57
## 63   44000.14             1   41889.29
## 64   38558.00             1   44000.14
## 65   43373.86             1   38558.00
## 66   49001.00             1   43373.86
## 67   61213.29             1   49001.00
## 68   58939.57             1   61213.29
## 69   42046.86             1   58939.57
## 70   39191.71             1   42046.86
## 71   42646.43             1   39191.71
## 72   36121.57             1   42646.43
## 73   30915.57             1   36121.57
## 74   20273.43             1   30915.57
## 75   23938.29             1   20273.43
## 76   19274.29             1   23938.29
## 77   21662.29             1   19274.29
## 78   15819.00             1   21662.29
## 79   18126.14             1   15819.00
## 80   17240.71             1   18126.14
## 81   16127.71             1   17240.71
## 82   13917.14             1   16127.71
## 83   15379.86             1   13917.14
## 84   19510.14             1   15379.86
## 85   24567.29             1   19510.14
## 86   25700.43             1   24567.29
## 87   25729.00             1   25700.43
## 88   26435.00             1   25729.00
## 89   31157.14             1   26435.00
## 90   29818.43             1   31157.14
## 91   30962.43             1   29818.43
## 92   28746.71             1   30962.43
## 93   27830.71             1   28746.71
## 94   28252.14             1   27830.71
## 95   28717.57             1   28252.14
## 96   21365.43             1   28717.57
## 97   24816.86             1   21365.43
## 98   16838.57             1   24816.86
## 99   15529.14             1   16838.57
## 100  13286.29             1   15529.14
## 101  13629.43             1   13286.29
## 102  14404.86             1   13629.43
## 103  19524.86             1   14404.86
## 104  18475.71             1   19524.86
## 105  22495.00             1   18475.71
## 106  22254.57             1   22495.00
## 107  24173.29             1   22254.57
## 108  27466.43             1   24173.29
## 109  24602.43             1   27466.43
## 110  20531.14             1   24602.43
## 111  20846.43             1   20531.14
## 112  23875.71             1   20846.43
## 113  36312.71             1   23875.71
## 114  34244.00             1   36312.71
## 115  36347.43             1   34244.00
## 116  39779.71             1   36347.43
## 117  42018.71             1   39779.71
## 118  39372.57             1   42018.71
## 119  33444.00             1   39372.57
## 120  29255.86             1   33444.00
## 121  31640.14             1   29255.86
## 122  29671.14             1   31640.14
## 123  31023.71             1   29671.14
## 124  39723.43             1   31023.71
## 125  39314.14             1   39723.43
## 126  38239.86             1   39314.14
## 127  34649.43             1   38239.86
## 128  36688.43             1   34649.43
## 129  42867.57             1   36688.43
## 130  42226.86             1   42867.57
## 131  32155.14             1   42226.86
## 132  33603.00             1   32155.14
## 133  37254.43             1   33603.00
## 134  33145.57             1   37254.43
## 135  31299.43             1   33145.57
## 136  30252.00             1   31299.43
## 137  26310.71             1   30252.00
## 138  27929.86             1   26310.71
## 139  27666.14             1   27929.86
## 140  25017.57             1   27666.14
## 141  27335.00             1   25017.57
## 142  25760.71             1   27335.00
## 143  18436.86             1   25760.71
## 144  21906.00             1   18436.86
## 145  19418.14             1   21906.00
## 146  22826.14             1   19418.14
## 147  23444.29             1   22826.14
## 148  25264.86             1   23444.29
## 149  25473.29             1   25264.86
## 150  27366.86             1   25473.29
## 151  28855.86             1   27366.86
## 152  32326.86             1   28855.86
## 153  27141.43             1   32326.86
## 154  26297.71             1   27141.43
## 155  23499.14             1   26297.71
## 156  30246.29             1   23499.14
## 157  39931.86             1   30246.29
## 158  38020.43             2   39931.86
## 159  35004.00             2   38020.43
## 160  40750.86             2   35004.00
## 161  42363.29             2   40750.86
## 162  46273.57             2   42363.29
## 163  41083.29             2   46273.57
## 164  35711.29             2   41083.29
## 165  41921.71             2   35711.29
## 166  60583.29             2   41921.71
## 167  63115.57             2   60583.29
## 168  61300.14             2   63115.57
## 169  57666.43             2   61300.14
## 170  55834.00             2   57666.43
## 171  58927.71             2   55834.00
## 172  57810.57             2   58927.71
## 173  48987.14             2   57810.57
## 174  52219.29             2   48987.14
## 175  56503.57             2   52219.29
## 176  56545.00             2   56503.57
## 177  64705.57             2   56545.00
## 178  53833.29             2   64705.57
## 179  50114.00             2   53833.29
## 180  39592.43             2   50114.00
## 181  29907.29             2   39592.43
## 182  33923.29             2   29907.29
## 183  45489.00             2   33923.29
## 184  44866.29             2   45489.00
## 185  51680.57             2   44866.29
## 186  58257.00             2   51680.57
## 187  70600.57             2   58257.00
## 188  76648.00             2   70600.57
## 189  69430.14             2   76648.00
## 190  69651.57             2   69430.14
## 191  77745.14             2   69651.57
## 192  72795.86             2   77745.14
## 193  67670.71             2   72795.86
## 194  55357.86             2   67670.71
## 195  48524.00             2   55357.86
## 196  50154.43             2   48524.00
## 197  45111.57             2   50154.43
## 198  36147.00             2   45111.57
## 199  43501.57             2   36147.00
## 200  41472.43             2   43501.57
## 201  41058.00             2   41472.43
## 202  41605.57             2   41058.00
## 203  49382.86             2   41605.57
## 204  59558.57             2   49382.86
## 205  59134.57             2   59558.57
## 206  61109.00             2   59134.57
## 207  63004.43             2   61109.00
## 208  67344.29             2   63004.43
## 209  78180.86             2   67344.29
## 210  69117.86             2   78180.86
## 211  55597.57             2   69117.86
## 212  49426.14             2   55597.57
## 213  39119.43             2   49426.14
## 214  35636.86             2   39119.43
## 215  39201.14             2   35636.86
## 216  27777.00             2   39201.14
## 217  47207.00             2   27777.00
## 218  55587.29             2   47207.00
## 219  56619.71             2   55587.29
## 220  82679.86             2   56619.71
## 221  91259.57             2   82679.86
## 222  93552.71             2   91259.57
## 223 102242.71             2   93552.71
## 224  91884.00             2  102242.71
## 225  85013.86             2   91884.00
## 226  84535.29             2   85013.86
## 227  80700.43             2   84535.29
## 228  79740.57             2   80700.43
## 229  85163.14             2   79740.57
## 230  86724.86             2   85163.14
## 231  80355.00             2   86724.86
## 232  74875.14             2   80355.00
## 233  81347.00             2   74875.14
## 234  66062.43             2   81347.00
## 235  56946.43             2   66062.43
## 236  47732.14             2   56946.43
## 237  38129.71             2   47732.14
## 238  42928.29             2   38129.71
## 239  45392.57             2   42928.29
## 240  37895.43             2   45392.57
## 241  30660.29             2   37895.43
## 242  42430.86             2   30660.29
## 243  35845.14             2   42430.86
## 244  40350.43             2   35845.14
## 245  31494.71             2   40350.43
## 246  30013.29             2   31494.71
## 247  34197.57             2   30013.29
## 248  37430.14             2   34197.57
## 249  26932.43             2   37430.14
## 250  33729.86             2   26932.43
## 251  38081.43             2   33729.86
## 252  44028.00             2   38081.43
## 253  47139.71             2   44028.00
## 254  46558.86             2   47139.71
## 255  58350.57             2   46558.86
## 256  78380.00             2   58350.57
## 257  78168.29             2   78380.00
## 258  70510.86             2   78168.29
## 259  72207.14             2   70510.86
## 260  67881.00             2   72207.14
## 261  69536.43             2   67881.00
## 262  62390.71             2   69536.43
## 263  50113.14             2   62390.71
## 264  45565.57             2   50113.14
## 265  45805.29             2   45565.57
## 266  41348.57             2   45805.29
## 267  51426.86             2   41348.57
## 268  47160.57             2   51426.86
## 269  51907.43             2   47160.57
## 270  49751.43             2   51907.43
## 271  54407.43             2   49751.43
## 272  54746.29             2   54407.43
## 273  61634.57             2   54746.29
## 274  58926.43             2   61634.57
## 275  69999.29             2   58926.43
## 276  63044.86             2   69999.29
## 277  63285.29             2   63044.86
## 278  61395.43             2   63285.29
## 279  67969.43             2   61395.43
## 280  60792.57             2   67969.43
## 281  56859.14             2   60792.57
## 282  44899.43             2   56859.14
## 283  43064.14             2   44899.43
## 284  62790.29             2   43064.14
## 285  69120.71             2   62790.29
## 286  69589.43             2   69120.71
## 287  66633.29             2   69589.43
## 288  65588.57             2   66633.29
## 289  70168.57             2   65588.57
## 290  74644.71             2   70168.57
## 291  52891.00             2   74644.71
## 292  41560.57             2   52891.00
## 293  34704.86             2   41560.57
## 294  46520.00             2   34704.86
## 295  50231.00             2   46520.00
## 296  49216.71             2   50231.00
## 297  76914.86             2   49216.71
## 298  83720.71             2   76914.86
## 299  84485.00             2   83720.71
## 300  89765.00             2   84485.00
## 301  87702.86             2   89765.00
## 302  82013.86             2   87702.86
## 303  85982.43             2   82013.86
## 304  57248.43             2   85982.43
## 305  52968.43             2   57248.43
## 306  52601.86             2   52968.43
## 307  45493.29             2   52601.86
## 308  42298.86             2   45493.29
## 309  46423.71             2   42298.86
## 310  37898.00             2   46423.71
## 311  36435.14             2   37898.00
## 312  30209.57             2   36435.14
## 313  34541.86             2   30209.57
## 314  33604.71             2   34541.86
## 315  37990.71             2   33604.71
## 316  35683.43             2   37990.71
## 317  65201.86             2   35683.43
## 318  62730.57             2   65201.86
## 319  64589.14             2   62730.57
## 320  73744.86             2   64589.14
## 321  76477.71             2   73744.86
## 322 105647.43             2   76477.71
## 323 103790.29             2  105647.43
## 324  76122.29             2  103790.29
## 325  74746.14             2   76122.29
## 326  72865.71             2   74746.14
## 327  63652.57             2   72865.71
## 328  60358.29             2   63652.57
## 329  25957.14             2   60358.29
## 330  30178.43             2   25957.14
## 331  30681.57             2   30178.43
## 332  33337.29             2   30681.57
## 333  32582.71             2   33337.29
## 334  39184.43             2   32582.71
## 335  40415.71             2   39184.43
## 336  34975.43             2   40415.71
## 337  34076.14             2   34975.43
## 338  34221.14             2   34076.14
## 339  28862.57             2   34221.14
## 340  35729.86             2   28862.57
## 341  36489.29             2   35729.86
## 342  36785.14             2   36489.29
## 343  37787.71             2   36785.14
## 344  39832.14             2   37787.71
## 345  41917.86             2   39832.14
## 346  41633.57             2   41917.86
## 347  33557.00             2   41633.57
## 348  22759.57             2   33557.00
## 349  28877.86             2   22759.57
## 350  27574.00             2   28877.86
## 351  27104.71             2   27574.00
## 352  24376.14             2   27104.71
## 353  29732.29             2   24376.14
## 354  34030.00             2   29732.29
## 355  39139.71             2   34030.00
## 356  37066.57             2   39139.71
## 357  38509.29             2   37066.57
## 358  40957.29             2   38509.29
## 359  49423.00             2   40957.29
## 360  50053.29             2   49423.00
## 361  50284.14             2   50053.29
## 362  53103.86             2   50284.14
## 363  50223.00             2   53103.86
## 364  49587.14             2   50223.00
## 365  41167.71             2   49587.14
## 366  37958.71             2   41167.71
## 367  33582.29             2   37958.71
## 368  31039.43             2   33582.29
## 369  26526.57             2   31039.43
## 370  34869.43             2   26526.57
## 371  37487.43             2   34869.43
## 372  46514.43             2   37487.43
## 373  39613.43             2   46514.43
## 374  38980.57             2   39613.43
## 375  37306.14             2   38980.57
## 376  36771.29             2   37306.14
## 377  26317.00             2   36771.29
## 378  31580.71             2   26317.00
## 379  23626.57             2   31580.71
## 380  33035.71             2   23626.57
## 381  44864.57             2   33035.71
## 382  48946.14             2   44864.57
## 383  46969.57             2   48946.14
## 384  49249.57             2   46969.57
## 385  56370.14             2   49249.57
## 386  67228.71             2   56370.14
## 387  59457.29             2   67228.71
## 388  53124.71             2   59457.29
## 389  52814.14             2   53124.71
## 390  61262.00             2   52814.14
## 391  61861.14             2   61262.00
## 392  71784.71             2   61861.14
## 393  59313.29             2   71784.71
## 394  61107.00             2   59313.29
## 395  60603.43             2   61107.00
## 396  60012.57             2   60603.43
## 397  58280.43             2   60012.57
## 398  56862.71             2   58280.43
## 399  41704.43             2   56862.71
## 400  51533.00             2   41704.43
## 401  50388.71             2   51533.00
## 402  49205.29             2   50388.71
## 403  56533.29             2   49205.29
## 404  47996.14             2   56533.29
## 405  47207.57             2   47996.14
## 406  45292.00             2   47207.57
## 407  40343.43             2   45292.00
## 408  39004.86             2   40343.43
## 409  36788.43             2   39004.86
## 410  30027.57             2   36788.43
## 411  39040.14             2   30027.57
## 412  42390.14             2   39040.14
## 413  36291.14             2   42390.14
## 414  30668.29             2   36291.14
## 415  47693.00             2   30668.29
## 416  52094.43             2   47693.00
## 417  56592.57             2   52094.43
## 418  47971.43             2   56592.57
## 419  43762.43             2   47971.43
## 420  42246.71             2   43762.43
## 421  46352.43             2   42246.71
## 422  33094.86             2   46352.43
## 423  32784.86             2   33094.86
## 424  26212.43             2   32784.86
## 425  32611.57             2   26212.43
## 426  42144.86             2   32611.57
## 427  50034.86             2   42144.86
## 428  46332.00             2   50034.86
## 429  42976.29             2   46332.00
## 430  39456.29             2   42976.29
## 431  39328.29             2   39456.29
## 432  35296.14             2   39328.29
## 433  30875.43             2   35296.14
## 434  27709.00             2   30875.43
## 435  29513.29             2   27709.00
## 436  31630.43             2   29513.29
## 437  29346.14             2   31630.43
## 438  34916.86             2   29346.14
## 439  42020.86             2   34916.86
## 440  38303.00             2   42020.86
## 441  37966.43             2   38303.00
## 442  41408.14             2   37966.43
## 443  38988.14             2   41408.14
## 444  43555.29             2   38988.14
## 445  38114.00             2   43555.29
## 446  27847.86             2   38114.00
## 447  26517.00             2   27847.86
## 448  39518.29             2   26517.00
## 449  39153.71             2   39518.29
## 450  45623.14             2   39153.71
## 451  40627.43             2   45623.14
## 452  41027.71             2   40627.43
## 453  42882.86             2   41027.71
## 454  47139.43             2   42882.86
## 455  35547.57             2   47139.43
## 456  41099.00             2   35547.57
## 457  35859.57             2   41099.00
## 458  44524.57             2   35859.57
## 459  48554.29             2   44524.57
## 460  51554.29             2   48554.29
## 461  47810.29             2   51554.29
## 462  50490.00             2   47810.29
## 463  50720.71             2   50490.00
## 464  52720.71             2   50720.71
## 465  52145.57             2   52720.71
## 466  55515.57             2   52145.57
## 467  52457.00             2   55515.57
## 468  58239.57             2   52457.00
## 469  50523.57             2   58239.57
## 470  47788.57             2   50523.57
## 471  46170.00             2   47788.57
## 472  42305.57             2   46170.00
## 473  46605.57             2   42305.57
## 474  55149.57             2   46605.57
## 475  48769.57             2   55149.57
## 476  50719.43             2   48769.57
## 477  44753.71             2   50719.43
## 478  42898.00             2   44753.71
## 479  46141.14             2   42898.00
## 480  34022.57             2   46141.14
## 481  26651.86             2   34022.57
## 482  28791.86             2   26651.86
## 483  31879.00             2   28791.86
## 484  33584.71             2   31879.00
## 485  34690.43             2   33584.71
## 486  27410.43             2   34690.43
## 487  41755.00             2   27410.43
## 488  49379.57             2   41755.00
## 489  57198.86             2   49379.57
## 490  51144.57             2   57198.86
## 491  56677.43             2   51144.57
## 492  65416.43             2   56677.43
## 493  69779.71             2   65416.43
## 494  54046.00             2   69779.71
## 495  43259.57             2   54046.00
## 496  40998.57             2   43259.57
## 497  41368.57             2   40998.57
## 498  42274.29             2   41368.57
## 499  35962.71             2   42274.29
## 500  38709.00             2   35962.71
## 501  44778.14             2   38709.00
## 502  51282.43             2   44778.14
## 503  52094.86             2   51282.43
## 504  52221.43             2   52094.86
## 505  45011.43             2   52221.43
## 506  46545.43             2   45011.43
## 507  42263.00             2   46545.43
## 508  45417.43             2   42263.00
## 509  45034.71             2   45417.43
## 510  37840.57             2   45034.71
## 511  39135.43             2   37840.57
## 512  38191.14             2   39135.43
## 513  39456.86             2   38191.14
## 514  42479.14             2   39456.86
## 515  34282.57             2   42479.14
## 516  28878.43             2   34282.57
## 517  56227.14             2   28878.43
## 518  65569.43             2   56227.14
## 519  69751.29             2   65569.43
## 520  62171.71             2   69751.29
## 521  63705.14             2   62171.71
## 522  79257.86             2   63705.14
## 523  87244.71             2   79257.86
## 524  58568.00             2   87244.71
## 525  52695.29             2   58568.00
## 526  48911.00             2   52695.29
## 527  53924.00             2   48911.00
## 528  53358.86             2   53924.00
## 529  42121.14             2   53358.86
## 530  47835.71             2   42121.14
## 531  62329.29             2   47835.71
## 532  56056.86             2   62329.29
## 533  59946.43             2   56056.86
## 534  64511.57             2   59946.43
## 535  61137.43             2   64511.57
## 536  55448.71             2   61137.43
## 537  47964.43             2   55448.71
## 538  46425.71             2   47964.43
## 539  55512.00             2   46425.71
## 540  55226.29             2   55512.00
## 541  46709.14             2   55226.29
## 542  49254.71             2   46709.14
## 543  49056.29             2   49254.71
## 544  49850.57             2   49056.29
## 545  39145.71             2   49850.57
## 546  29799.43             2   39145.71
## 547  34769.86             2   29799.43
## 548  44061.57             2   34769.86
## 549  43829.14             2   44061.57
## 550  45782.00             2   43829.14
## 551  38924.57             2   45782.00
## 552  49242.43             2   38924.57
## 553  50565.00             2   49242.43
## 554  38864.43             2   50565.00
## 555  49786.71             2   38864.43
## 556  58787.86             2   49786.71
## 557  58060.86             2   58787.86
## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 564  70525.00             2   65466.00
## 565  68377.86             2   70525.00
## 566  69736.29             2   68377.86
## 567  60085.86             2   69736.29
## 568  41757.00             2   60085.86
## 569  49780.29             2   41757.00
## 570  56540.29             2   49780.29
## 571  57894.29             2   56540.29
## 572  60270.29             2   57894.29
## 573  61011.00             2   60270.29
## 574  57721.43             2   61011.00
## 575  71741.00             2   57721.43
## 576  59576.00             2   71741.00
## 577  52390.29             2   59576.00
## 578  61092.29             2   52390.29
## 579  62814.00             2   61092.29
## 580  54908.29             2   62814.00
## 581  62082.00             2   54908.29
## 582  57017.71             2   62082.00
## 583  53634.43             2   57017.71
## 584  69169.00             2   53634.43
## 585  52488.14             2   69169.00
## 586  60895.57             2   52488.14
## 587  59856.57             2   60895.57
## 588  52670.00             2   59856.57
## 589  51874.57             2   52670.00
## 590  52190.57             2   51874.57
## 591  41562.43             2   52190.57
## 592  44764.14             2   41562.43
## 593  38612.71             2   44764.14
## 594  43473.14             2   38612.71
## 595  53505.00             2   43473.14
## 596  45870.86             2   53505.00
## 597  52578.00             2   45870.86
## 598  55300.00             2   52578.00
## 599  61789.71             2   55300.00
## 600  57391.71             2   61789.71
## 601  62902.29             2   57391.71
## 602  53250.43             2   62902.29
## 603  55402.57             2   53250.43
## 604  56291.29             2   55402.57
## 605  58933.57             2   56291.29
## 606  59590.71             2   58933.57
## 607  59065.00             2   59590.71
## 608  52399.57             2   59065.00
## 609  60483.43             2   52399.57
## 610  58262.71             2   60483.43
## 611  54939.71             2   58262.71
## 612  51169.00             2   54939.71
## 613  43113.29             2   51169.00
## 614  56289.71             2   43113.29
## 615  60739.86             2   56289.71
## 616  50363.14             2   60739.86
## 617  62270.86             2   50363.14
## 618  67061.57             2   62270.86
## 619  59609.00             2   67061.57
## 620  85054.00             2   59609.00
## 621  68023.29             2   85054.00
## 622  59242.29             2   68023.29
## 623  61535.14             2   59242.29
## 624  56215.86             2   61535.14
## 625  45152.29             2   56215.86
## 626  57409.57             2   45152.29
## 627  35151.43             2   57409.57
## 628  34991.43             2   35151.43
## 629  45944.71             2   34991.43
## 630  57944.71             2   45944.71
## 631  55706.29             2   57944.71
## 632  88593.71             2   55706.29
## 633  77359.43             2   88593.71
## 634  79878.71             2   77359.43
## 635  81753.00             2   79878.71
## 636  75716.00             2   81753.00
## 637  67381.43             2   75716.00
## 638  63528.57             2   67381.43
## 639  49682.86             2   63528.57
## 640  47815.00             2   49682.86
## 641  46546.14             2   47815.00
## 642  44808.71             2   46546.14
## 643  42959.57             2   44808.71
## 644  46023.86             2   42959.57
## 645  51309.57             2   46023.86
## 646  68447.29             2   51309.57
## 647  84959.29             2   68447.29
## 648  81666.29             2   84959.29
## 649  82700.86             2   81666.29
## 650  89422.14             2   82700.86
## 651 104812.71             2   89422.14
## 652  98812.71             2  104812.71
## 653  64779.86             2   98812.71
## 654  61862.86             2   64779.86
## 655  58376.43             2   61862.86
## 656  59503.57             2   58376.43
## 657  55429.43             2   59503.57
## 658  44454.57             2   55429.43
## 659  47184.00             2   44454.57
## 660  52126.71             2   47184.00
## 661  51202.00             2   52126.71
## 662  64437.14             2   51202.00
## 663  64297.14             2   64437.14
## 664  64628.57             2   64297.14
## 665  51413.14             2   64628.57
## 666  52969.43             2   51413.14
## 667  54135.29             2   52969.43
## 668  48799.43             2   54135.29
## 669  41907.86             2   48799.43
## 670  45382.00             2   41907.86
## 671  42633.29             2   45382.00
## 672  46624.71             2   42633.29
## 673  44051.86             2   46624.71
## 674  35852.86             2   44051.86
## 675  29737.71             2   35852.86
## 676  29734.86             2   29737.71
## 677  32881.71             2   29734.86
## 678  38298.57             2   32881.71
## 679  40886.14             2   38298.57
## 680  38601.86             2   40886.14
## 681  38628.86             2   38601.86
## 682  39142.57             2   38628.86
## 683  32666.14             2   39142.57
## 684  39911.57             2   32666.14
## 685  39336.29             2   39911.57
## 686  39678.86             2   39336.29
## 687  41963.14             2   39678.86
## 688  54220.57             2   41963.14
## 689  63901.86             2   54220.57
## 690  73116.00             2   63901.86
## 691  60863.86             2   73116.00
## 692  56293.86             2   60863.86
## 693  52725.00             2   56293.86
## 694  58625.00             2   52725.00
## 695  47513.00             2   58625.00
## 696  40300.14             2   47513.00
## 697  33312.43             2   40300.14
## 698  29556.71             2   33312.43
## 699  27816.71             2   29556.71
## 700  34120.29             2   27816.71
## 701  32132.57             2   34120.29
## 702  32902.57             2   32132.57
## 703  39694.14             2   32902.57
## 704  72501.29             2   39694.14
## 705  79551.14             2   72501.29
## 706  99637.71             2   79551.14
## 707  95424.29             2   99637.71
## 708  98395.14             2   95424.29
## 709 115594.71             2   98395.14
## 710 114267.57             2  115594.71
## 711  88353.29             2  114267.57
## 712  88750.86             2   88353.29
## 713  78835.71             2   88750.86
## 714  75519.14             2   78835.71
## 715  73202.86             2   75519.14
## 716  53433.29             2   73202.86
## 717  48165.71             2   53433.29
## 718  52163.14             2   48165.71
## 719  49306.86             2   52163.14
## 720  36846.86             2   49306.86
## 721  43220.57             2   36846.86
## 722  38952.29             2   43220.57
## 723  41522.29             2   38952.29
## 724  39090.00             2   41522.29
## 725  28452.57             2   39090.00
## 726  32975.00             2   28452.57
## 727  33690.71             2   32975.00
## 728  26405.29             2   33690.71
## 729  47087.43             2   26405.29
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   572 51869.58 16246.254
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  58625.00
## [694]  47513.00  40300.14  33312.43  29556.71  27816.71  34120.29  32132.57
## [701]  32902.57  39694.14  72501.29  79551.14  99637.71  95424.29  98395.14
## [708] 115594.71 114267.57  88353.29  88750.86  78835.71  75519.14  73202.86
## [715]  53433.29  48165.71  52163.14  49306.86  36846.86  43220.57  38952.29
## [722]  41522.29  39090.00  28452.57  32975.00  33690.71  26405.29  47087.43
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [704] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   1980.73574   4024.02413   -521.80939   2452.16968  -2937.47365    530.43462 
##            8            9           10           11           12           13 
##  -5638.66267  -1207.10116  -3987.43803   -459.56440  -4975.36512  -1670.02753 
##           14           15           16           17           18           19 
##   -959.96305    322.37072  -3284.98253   -432.82021  -2177.08983   6552.38201 
##           20           21           22           23           24           25 
##  -1527.00901  -1213.12502   1466.79719  -1181.00568    235.09475   1700.44532 
##           26           27           28           29           30           31 
##  -7082.48344    920.87134   8179.01459    464.81738     33.15415  -2355.85874 
##           32           33           34           35           36           37 
##   1602.88065   4609.95185   1193.78442   2461.04162  -1787.57695   4669.38625 
##           38           39           40           41           42           43 
##   4340.00567  -2214.69434  -2942.97220  -1096.10264 -10736.27664   7222.51992 
##           44           45           46           47           48           49 
##   2547.74280   1375.87809   8122.47092    755.64583   6594.37824   6815.60408 
##           50           51           52           53           54           55 
##  -5748.78069  -4717.63858  -5023.40192  -7930.24785   6075.77821  -4082.66024 
##           56           57           58           59           60           61 
##  -4926.50941   3795.32585    860.96143    -49.40529    127.17807  -5008.35522 
##           62           63           64           65           66           67 
##  18083.32702   3723.85775  -3548.80315   5986.43575   7437.30670  14769.78017 
##           68           69           70           71           72           73 
##   1905.67165 -13015.29621  -1221.21547   4709.45533  -4811.30177  -4358.99905 
##           74           75           76           77           78           79 
## -10486.54131   2407.09504  -5435.03909    997.54355  -6916.59623    457.79619 
##           80           81           82           83           84           85 
##  -2428.36779  -2773.53138  -4018.91831   -639.21458   2222.61728   3698.01016 
##           86           87           88           89           90           91 
##    445.64012   -508.44067    172.78242   4282.68786  -1151.03007   1153.89200 
##           92           93           94           95           96           97 
##  -2053.88972  -1048.44042    167.33585    267.30501  -7488.45369   2338.69300 
##           98           99          100          101          102          103 
##  -8632.64333  -3023.36781  -4130.69921  -1842.56899  -1364.71110   3082.84359 
##          104          105          106          107          108          109 
##  -2406.32137   2522.77244  -1203.14781    924.06416   2553.31369  -3166.47284 
##          110          111          112          113          114          115 
##  -4754.12119   -908.24982   1847.62270  11657.65092  -1196.32800   2701.07267 
##          116          117          118          119          120          121 
##   4309.28234   3571.83226  -1015.95308  -4649.81107  -3696.74530   2319.46363 
##          122          123          124          125          126          127 
##  -1717.16943   1342.90268   8869.67809    916.07134    196.71485  -2462.10194 
##          128          129          130          131          132          133 
##   2690.48833   7101.42708   1102.21067  -8413.88143   1768.08392   4163.94263 
##          134          135          136          137          138          139 
##  -3111.40349  -1394.37906   -840.84765  -3873.81188   1163.18170   -504.64002 
##          140          141          142          143          144          145 
##  -2924.52058   1689.72746  -1894.21335  -7852.86280   1967.46904  -3528.80040 
##          146          147          148          149          150          151 
##   2036.64895   -300.59790    983.92510   -386.43097   1326.39290   1173.30326 
##          152          153          154          155          156          157 
##   3353.05464  -4842.39673  -1189.34979  -3256.25909   5917.78201   9752.28636 
##          158          159          160          161          162          163 
##  -3507.70429  -4866.55764   3496.12167    124.92276   2636.92361  -5944.32988 
##          164          165          166          167          168          169 
##  -6815.35658   4053.62643  17329.56502   3678.68870   -332.71732  -2392.10680 
##          170          171          172          173          174          175 
##  -1073.40808   3609.37327   -190.61330  -8045.26473   2838.48318   4319.88106 
##          176          177          178          179          180          181 
##    646.01212   8770.65703  -9178.40945  -3469.33796 -10765.57525 -11326.49746 
##          182          183          184          185          186          187 
##   1088.37917   9171.45112  -1480.95596   5873.34249   6540.47825  13181.02464 
##          188          189          190          191          192          193 
##   8524.20908  -3937.92864   2542.76658  10444.31697  -1523.64766  -2356.81045 
##          194          195          196          197          198          199 
## -10225.18564  -6381.43383   1175.25974  -5281.49172  -9872.93870   5255.63571 
##          200          201          202          203          204          205 
##  -3151.33130  -1806.10379   -899.14330   6403.29295   9834.60871    586.31255 
##          206          207          208          209          210          211 
##   2928.43045   3111.65072   5807.80772  12880.89054  -5579.49552 -11240.42180 
##          212          213          214          215          216          217 
##  -5687.16924 -10642.07117  -5186.74431   1397.59885 -13117.46341  16219.45979 
##          218          219          220          221          222          223 
##   7750.20850   1515.32188  26680.15113  12660.72278   7513.60761  14215.01289 
##          224          225          226          227          228          229 
##  -3679.59828  -1566.74888   3912.41136    492.56745   2858.26704   9113.21873 
##          230          231          232          233          234          235 
##   5972.52344  -1751.63955  -1707.60870   9516.33571 -11380.57727  -7241.92200 
##          236          237          238          239          240          241 
##  -8550.88711 -10162.76251   2962.95639   1265.96023  -8368.19103  -9101.87295 
##          242          243          244          245          246          247 
##   8942.95516  -7850.10235   2366.26090 -10396.40029  -4198.22590   1270.74255 
##          248          249          250          251          252          253 
##    874.73565 -12426.23813   3474.72237   1931.61936   4104.54366   2059.43986 
##          254          255          256          257          258          259 
##  -1219.87045  11075.55840  20879.30883   3298.23799  -4175.59368   4161.15143 
##          260          261          262          263          264          265 
##  -1635.99652   3771.02774  -4810.26066 -10891.12713  -4791.68909   -608.35807 
##          266          267          268          269          270          271 
##  -5272.95062   8670.16125  -4335.93137   4110.61384  -2161.82183   4363.84372 
##          272          273          274          275          276          277 
##    665.05577   7259.48734  -1422.12052  11999.21595  -4557.50373   1713.74839 
##          278          279          280          281          282          283 
##   -384.60644   7828.26221  -5049.51394  -2759.23073 -11307.90794  -2771.82729 
##          284          285          286          287          288          289 
##  18545.86032   7769.93922   2748.95765   -613.65040    905.17821   6391.14589 
##          290          291          292          293          294          295 
##   6895.55023 -18739.83852 -11205.62407  -8235.68342   9524.67877   2989.68357 
##          296          297          298          299          300          301 
##  -1242.75098  27334.97215  10121.22646   4983.52855   9600.74624   2959.83058 
##          302          303          304          305          306          307 
##   -940.89600   7961.12967 -24214.38300  -3576.49333   -231.48378  -7022.16747 
##          308          309          310          311          312          313 
##  -4052.10106   2842.93840  -9259.81825  -3329.24573  -8286.23941   1444.81090 
##          314          315          316          317          318          319 
##  -3249.25468   1949.42794  -4161.36109  27357.92678   -711.49864   3290.15153 
##          320          321          322          323          324          325 
##  10834.12788   5627.22452  32427.02745   5274.14707 -20783.35385   1833.96649 
##          326          327          328          329          330          331 
##   1146.91774  -6435.53285  -1740.25647 -33284.62171    769.05359  -2388.46798 
##          332          333          334          335          336          337 
##   -169.07507  -3226.66019   4029.41226   -464.25463  -6972.30124  -3153.81570 
##          338          339          340          341          342          343 
##  -2228.96249  -7713.27673   3800.91828  -1394.90713  -1757.62023  -1011.61369 
##          344          345          346          347          348          349 
##    163.39314    476.19570  -1616.80438  -9446.84556 -13240.33748   2241.38993 
##          350          351          352          353          354          355 
##  -4368.19451  -3706.78598  -6028.39668   1693.94099   1346.85202   2729.62365 
##          356          357          358          359          360          361 
##  -3774.62163   -534.09482    662.79516   7005.62388    294.51144    -21.21003 
##          362          363          364          365          366          367 
##   2598.30683  -2727.78347   -865.38487  -8733.40333  -4641.14377  -6234.75380 
##          368          369          370          371          372          373 
##  -4982.40790  -7290.12014   4966.24979    349.39234   7106.08417  -7623.05636 
##          374          375          376          377          378          379 
##  -2271.42278  -3397.04281  -2479.84920 -12470.31118   1859.27413 -10659.51868 
##          380          381          382          383          384          385 
##   5647.39177   9316.71722   3140.40053  -2375.67624   1618.39033   6761.76442 
##          386          387          388          389          390          391 
##  11445.43457  -5742.45829  -5335.71045   -154.72776   8562.45439   1835.68466 
##          392          393          394          395          396          397 
##  11239.68430  -9837.38421   2771.45088    712.38510    558.22098   -661.53539 
##          398          399          400          401          402          403 
##   -577.14957 -14506.00506   8467.70770  -1199.83473  -1390.94809   6963.31148 
##          404          405          406          407          408          409 
##  -7928.61301  -1313.84401  -2545.57275  -5832.97631  -2880.18709  -3935.81747 
##          410          411          412          413          414          415 
##  -8774.60590   6100.92552   1635.29733  -7368.79526  -7702.64951  14198.16048 
##          416          417          418          419          420          421 
##   3835.89614   4517.15615  -8004.73938  -4737.55484  -2603.25877   2816.87054 
##          422          423          424          425          426          427 
## -14001.14272  -2814.28526  -9117.88437   2980.81476   6964.81615   6587.62879 
##          428          429          430          431          432          433 
##  -3957.37179  -4101.99866  -4711.95069  -1787.43549  -5708.57780  -6632.65079 
##          434          435          436          437          438          439 
##  -5965.47211  -1415.28545   -862.80440  -4983.05907   2568.56909   4841.69125 
##          440          441          442          443          444          445 
##  -5036.69656  -2149.17270   1584.41357  -3820.21289   2845.53416  -6556.34046 
##          446          447          448          449          450          451 
## -12103.84488  -4531.98694   9623.40720  -2015.77281   4769.80912  -5836.14069 
##          452          453          454          455          456          457 
##  -1103.61242    404.40594   3052.21266 -12230.90839   3372.88384  -6680.69816 
##          458          459          460          461          462          463 
##   6527.89142   3043.38861   2548.85336  -3796.72209   2129.75835     36.64634 
##          464          465          466          467          468          469 
##   1836.57280   -472.95369   3395.80549  -2585.20236   5849.73718  -6880.86145 
##          470          471          472          473          474          475 
##  -2924.60939  -2171.41120  -4632.22787   3018.97297   7834.04815  -5955.23873 
##          476          477          478          479          480          481 
##   1527.30220  -6129.31224  -2811.60792   2040.79519 -12890.20319  -9751.79150 
##          482          483          484          485          486          487 
##  -1219.96839     11.38398   -960.04675  -1333.51394  -9572.38032  11085.34754 
##          488          489          490          491          492          493 
##   6270.42400   7477.74377  -5357.36253   5425.72164   9366.67320   6151.56963 
##          494          495          496          497          498          499 
## -13365.95033 -10508.23062  -3415.32803  -1084.60733   -499.75402  -7596.75346 
##          500          501          502          503          504          505 
##    622.87535   4310.46171   5551.63640    723.60162    145.64164  -7174.12006 
##          506          507          508          509          510          511 
##    612.33294  -5000.36788   1867.74770  -1250.46123  -8112.71739   -579.15843 
##          512          513          514          515          516          517 
##  -2646.33366   -561.74253   1362.92611  -9454.54676  -7750.68994  24284.45281 
##          518          519          520          521          522          523 
##   9910.15729   5990.46071  -5215.58302   2890.78787  17113.72545  11613.39603 
##          524          525          526          527          528          529 
## -23989.45541  -4993.95773  -3685.47367   4609.22793   -303.14750 -11050.77453 
##          530          531          532          533          534          535 
##   4409.05077  13946.99262  -4894.14249   4434.82766   5626.96601  -1706.03138 
##          536          537          538          539          540          541 
##  -4468.71659  -7019.79583  -2068.19878   8352.44737    187.18046  -8082.19330 
##          542          543          544          545          546          547 
##   1849.37471   -556.55257    409.80878 -10983.84644 -11046.96763   2028.48343 
##          548          549          550          551          552          553 
##   7009.88273  -1280.24444    874.17286  -7676.75744   8587.80564    962.81579 
##          554          555          556          557          558          559 
## -11884.67876   9184.24675   8713.67281    180.95538   4929.97526  -3487.18761 
##          560          561          562          563          564          565 
##  14177.99514  21607.58211  -6303.44121  -9558.53175   6853.86740    319.60114 
##          566          567          568          569          570          571 
##   3540.01443  -7288.43228 -17248.51674   6669.40391   6471.67619   1963.45951 
##          572          573          574          575          576          577 
##   3165.28179   1845.54831  -2086.36448  14785.89637  -9536.76125  -6173.08708 
##          578          579          580          581          582          583 
##   8760.30555   2935.71664  -6463.05418   7566.43084  -3718.84120  -2710.41979 
##          584          585          586          587          588          589 
##  15758.10933 -14394.20104   8478.73035    148.87709  -6136.68204   -699.97468 
##          590          591          592          593          594          595 
##    305.81447 -10596.36100   1821.99180  -7105.93720   3088.95989   8905.89315 
##          596          597          598          599          600          601 
##  -7427.79413   5899.61494   2805.23554   6934.45370  -3091.37342   6233.10762 
##          602          603          604          605          606          607 
##  -8197.47198   2324.68241   1347.07602   3218.67598   1584.45029    488.86710 
##          608          609          610          611          612          613 
##  -5720.66635   8143.39591  -1087.57314  -2484.78788  -3373.82376  -8159.60548 
##          614          615          616          617          618          619 
##  12002.67260   5026.32443  -9209.51733  11696.79867   6161.24057  -5445.79908 
##          620          621          622          623          624          625 
##  26462.00987 -12634.40019  -6646.50413   3261.16436  -4046.46831 -10497.19869 
##          626          627          628          629          630          631 
##  11354.32563 -21533.23509  -2391.15575   8700.88066  11202.28092  -1442.44946 
##          632          633          634          635          636          637 
##  33386.12605  -6367.86860   5893.69776   5583.27951  -2079.08572  -5178.42015 
##          638          639          640          641          642          643 
##  -1803.60515 -12308.15325  -2169.12027  -1818.18698  -2455.27302  -2797.73204 
##          644          645          646          647          648          649 
##   1870.11523   4498.50602  17052.49213  18702.80654   1090.73526   4980.96934 
##          650          651          652          653          654          655 
##  10805.08318  20367.01064   1020.43303 -27809.27321  -1213.25798  -2170.08802 
##          656          657          658          659          660          661 
##   1980.45716  -3071.13476 -10512.92856   1733.80632   4309.58249   -901.41318 
##          662          663          664          665          666          667 
##  13135.63434   1518.22675   1971.06218 -11531.77854   1484.81869   1301.07760 
##          668          669          670          671          672          673 
##  -5045.80132  -7310.16109   2140.29611  -3621.16643   2753.92468  -3280.26666 
##          674          675          676          677          678          679 
##  -9248.10600  -8253.14315  -2952.99845    196.33639   2884.26476    774.38201 
##          680          681          682          683          684          685 
##  -3753.82448  -1745.91060  -1255.61049  -8177.52789   4684.20654  -2174.25554 
##          686          687          688          689          690          691 
##  -1332.80105    654.40953  10930.92422   9982.66818  10801.27929  -9441.29285 
##          692          693          694          695          696          697 
##  -3386.33483  -2992.12537   6002.75834 -10225.67338  -7802.29505  -8535.07864 
##          698          699          700          701          702          703 
##  -6231.10428  -4714.17959   3098.30560  -4355.81424  -1862.08468   4261.74905 
##          704          705          706          707          708          709 
##  31179.29674   9779.06806  23752.06106   2119.72212   8744.43004  23367.69846 
##          710          711          712          713          714          715 
##   7125.22801 -17638.17028   5232.05766  -5027.85589    253.90344    813.72132 
##          716          717          718          719          720          721 
## -16947.18609  -5070.74695   3494.67646  -2828.14660 -12811.19900   4367.72533 
##          722          723          724          725          726          727 
##  -5427.79326    843.62918  -3817.33950 -12345.50980   1401.60978  -1804.48899 
##          728          729 
##  -9710.57913  17289.42777 
## 
## $fitted.values
##         2         3         4         5         6         7         8         9 
##  17288.55  20114.98  24337.95  24057.97  26394.19  23746.28  24457.38  19724.24 
##        10        11        12        13        14        15        16        17 
##  19462.72  16824.85  17596.65  14349.88  14400.68  15060.49  16744.70  15076.96 
##        18        19        20        21        22        23        24        25 
##  16104.09  15482.19  22513.01  21603.70  21087.35  22963.58  22294.48  22942.27 
##        26        27        28        29        30        31        32        33 
##  24774.77  18747.41  20460.99  28241.18  28298.42  27973.72  25620.41  27012.62 
##        34        35        36        37        38        39        40        41 
##  30827.64  31173.53  32572.43  30101.19  34102.99  37287.69  34365.26  31199.39 
##        42        43        44        45        46        47        48        49 
##  30055.56  20703.77  28167.69  30586.41  31667.67  38455.93  37954.19  42582.40 
##        50        51        52        53        54        55        56        57 
##  46787.78  39538.92  34146.97  29205.96  22400.36  28644.52  25250.08  21574.67 
##        58        59        60        61        62        63        64        65 
##  25950.90  27201.26  27496.11  27904.93  23805.96  40276.29  42106.80  37387.42 
##        66        67        68        69        70        71        72        73 
##  41563.69  46443.51  57033.90  55062.15  40412.93  37936.97  40932.87  35274.57 
##        74        75        76        77        78        79        80        81 
##  30759.97  21531.19  24709.32  20664.74  22735.60  17668.35  19669.08  18901.25 
##        82        83        84        85        86        87        88        89 
##  17936.06  16019.07  17287.53  20869.28  25254.79  26237.44  26262.22  26874.45 
##        90        91        92        93        94        95        96        97 
##  30969.46  29808.54  30800.60  28879.15  28084.81  28450.27  28853.88  22478.16 
##        98        99       100       101       102       103       104       105 
##  25471.21  18552.51  17416.98  15472.00  15769.57  16442.01  20882.04  19972.23 
##       106       107       108       109       110       111       112       113 
##  23457.72  23249.22  24913.11  27768.90  25285.26  21754.68  22028.09  24655.06 
##       114       115       116       117       118       119       120       121 
##  35440.33  33646.36  35470.43  38446.88  40388.52  38093.81  32952.60  29320.68 
##       122       123       124       125       126       127       128       129 
##  31388.31  29680.81  30853.75  38398.07  38043.14  37111.53  33997.94  35766.14 
##       130       131       132       133       134       135       136       137 
##  41124.65  40569.02  31834.92  33090.49  36256.97  32693.81  31092.85  30184.53 
##       138       139       140       141       142       143       144       145 
##  26766.68  28170.78  27942.09  25645.27  27654.93  26289.72  19938.53  22946.94 
##       146       147       148       149       150       151       152       153 
##  20789.49  23744.88  24280.93  25859.72  26040.46  27682.55  28973.80  31983.83 
##       154       155       156       157       158       159       160       161 
##  27487.06  26755.40  24328.50  30179.57  41528.13  39870.56  37254.74  42238.36 
##       162       163       164       165       166       167       168       169 
##  43636.65  47027.62  42526.64  37868.09  43253.72  59436.88  61632.86  60058.54 
##       170       171       172       173       174       175       176       177 
##  56907.41  55318.34  58001.18  57032.41  49380.80  52183.69  55898.99  55934.91 
##       178       179       180       181       182       183       184       185 
##  63011.70  53583.34  50358.00  41233.78  32834.91  36317.55  46347.24  45807.23 
##       186       187       188       189       190       191       192       193 
##  51716.52  57419.55  68123.79  73368.07  67108.80  67300.83  74319.50  70027.52 
##       194       195       196       197       198       199       200       201 
##  65583.04  54905.43  48979.17  50393.06  46019.94  38245.94  44623.76  42864.10 
##       202       203       204       205       206       207       208       209 
##  42504.71  42979.56  49723.96  58548.26  58180.57  59892.78  61536.48  65299.97 
##       210       211       212       213       214       215       216       217 
##  74697.35  66837.99  55113.31  49761.50  40823.60  37803.54  40894.46  30987.54 
##       218       219       220       221       222       223       224       225 
##  47837.08  55104.39  55999.71  78598.85  86039.11  88027.70  95563.60  86580.61 
##       226       227       228       229       230       231       232       233 
##  80622.87  80207.86  76882.30  76049.92  80752.33  82106.64  76582.75  71830.66 
##       234       235       236       237       238       239       240       241 
##  77443.01  64188.35  56283.03  48292.48  39965.33  44126.61  46263.62  39762.16 
##       242       243       244       245       246       247       248       249 
##  33487.90  43695.25  37984.17  41891.11  34211.51  32926.83  36555.41  39358.67 
##       250       251       252       253       254       255       256       257 
##  30255.13  36149.81  39923.46  45080.27  47778.73  47275.01  57500.69  74870.05 
##       258       259       260       261       262       263       264       265 
##  74686.45  68045.99  69517.00  65765.40  67200.97  61004.27  50357.26  46413.64 
##       266       267       268       269       270       271       272       273 
##  46621.52  42756.70  51496.50  47796.81  51913.25  50043.58  54081.23  54375.08 
##       274       275       276       277       278       279       280       281 
##  60348.55  58000.07  67602.36  61571.54  61780.04  60141.17  65842.09  59618.37 
##       282       283       284       285       286       287       288       289 
##  56207.34  45835.97  44244.43  61350.78  66840.47  67246.94  64683.39  63777.43 
##       290       291       292       293       294       295       296       297 
##  67749.16  71630.84  52766.20  42940.54  36995.32  47241.32  50459.47  49579.88 
##       298       299       300       301       302       303       304       305 
##  73599.49  79501.47  80164.25  84743.03  82954.75  78021.30  81462.81  56544.92 
##       306       307       308       309       310       311       312       313 
##  52833.34  52515.45  46350.96  43580.78  47157.82  39764.39  38495.81  33097.05 
##       314       315       316       317       318       319       320       321 
##  36853.97  36041.29  39844.79  37843.93  63442.07  61298.99  62910.73  70850.49 
##       322       323       324       325       326       327       328       329 
##  73220.40  98516.14  96905.64  72912.18  71718.80  70088.10  62098.54  59241.76 
##       330       331       332       333       334       335       336       337 
##  29409.37  33070.04  33506.36  35809.37  35155.02  40879.97  41947.73  37229.96 
##       338       339       340       341       342       343       344       345 
##  36450.11  36575.85  31928.94  37884.19  38542.76  38799.33  39668.75  41441.66 
##       346       347       348       349       350       351       352       353 
##  43250.38  43003.85  35999.91  26636.47  31942.19  30811.50  30404.54  28038.34 
##       354       355       356       357       358       359       360       361 
##  32683.15  36410.09  40841.19  39043.38  40294.49  42417.38  49758.77  50305.35 
##       362       363       364       365       366       367       368       369 
##  50505.55  52950.78  50452.53  49901.12  42599.86  39817.04  36021.84  33816.69 
##       370       371       372       373       374       375       376       377 
##  29903.18  37138.04  39408.34  47236.48  41251.99  40703.19  39251.13  38787.31 
##       378       379       380       381       382       383       384       385 
##  29721.44  34286.09  27388.32  35547.85  45805.74  49345.25  47631.18  49608.38 
##       386       387       388       389       390       391       392       393 
##  55783.28  65199.74  58460.42  52968.87  52699.55  60025.46  60545.03  69150.67 
##       394       395       396       397       398       399       400       401 
##  58335.55  59891.04  59454.35  58941.96  57439.86  56210.43  43065.29  51588.55 
##       402       403       404       405       406       407       408       409 
##  50596.23  49569.97  55924.76  48521.42  47837.57  46176.40  41885.04  40724.25 
##       410       411       412       413       414       415       416       417 
##  38802.18  32939.22  40754.85  43659.94  38370.94  33494.84  48258.53  52075.42 
##       418       419       420       421       422       423       424       425 
##  55976.17  48499.98  44849.97  43535.56  47096.00  35599.14  35330.31  29630.76 
##       426       427       428       429       430       431       432       433 
##  35180.04  43447.23  50289.37  47078.28  44168.24  41115.72  41004.72  37508.08 
##       434       435       436       437       438       439       440       441 
##  33674.47  30928.57  32493.23  34329.20  32348.29  37179.17  43339.70  40115.60 
##       442       443       444       445       446       447       448       449 
##  39823.73  42808.36  40709.75  44670.34  39951.70  31048.99  29894.88  41169.49 
##       450       451       452       453       454       455       456       457 
##  40853.33  46463.57  42131.33  42478.45  44087.22  47778.48  37726.12  42540.27 
##       458       459       460       461       462       463       464       465 
##  37996.68  45510.90  49005.43  51607.01  48360.24  50684.07  50884.14  52618.53 
##       466       467       468       469       470       471       472       473 
##  52119.77  55042.20  52389.83  57404.43  50713.18  48341.41  46937.80  43586.60 
##       474       475       476       477       478       479       480       481 
##  47315.52  54724.81  49192.13  50883.03  45709.61  44100.35  46912.77  36403.65 
##       482       483       484       485       486       487       488       489 
##  30011.83  31867.62  34544.76  36023.94  36982.81  30669.65  43109.15  49721.11 
##       490       491       492       493       494       495       496       497 
##  56501.93  51251.71  56049.76  63628.14  67411.95  53767.80  44413.90  42453.18 
##       498       499       500       501       502       503       504       505 
##  42774.04  43559.47  38086.12  40467.68  45730.79  51371.26  52075.79  52185.55 
##       506       507       508       509       510       511       512       513 
##  45933.10  47263.37  43549.68  46285.18  45953.29  39714.59  40837.48  40018.60 
##       514       515       516       517       518       519       520       521 
##  41116.22  43737.12  36629.12  31942.69  55659.27  63760.83  67387.30  60814.35 
##       522       523       524       525       526       527       528       529 
##  62144.13  75631.32  82557.46  57689.24  52596.47  49314.77  53662.00  53171.92 
##       530       531       532       533       534       535       536       537 
##  43426.66  48382.29  60951.00  55511.60  58884.61  62843.46  59917.43  54984.22 
##       538       539       540       541       542       543       544       545 
##  48493.91  47159.55  55039.11  54791.34  47405.34  49612.84  49440.76  50129.56 
##       546       547       548       549       550       551       552       553 
##  40846.40  32741.37  37051.69  45109.39  44907.83  46601.33  40654.62  49602.18 
##       554       555       556       557       558       559       560       561 
##  50749.11  40602.47  50074.18  57879.90  57249.45  60821.04  56619.00  68294.13 
##       562       563       564       565       566       567       568       569 
##  84861.58  75024.53  63671.13  68058.26  66196.27  67374.29  59005.52  43110.88 
##       570       571       572       573       574       575       576       577 
##  50068.61  55930.83  57105.00  59165.45  59807.79  56955.10  69112.76  58563.37 
##       578       579       580       581       582       583       584       585 
##  52331.98  59878.28  61371.34  54515.57  60736.56  56344.85  53410.89  66882.34 
##       586       587       588       589       590       591       592       593 
##  52416.84  59707.69  58806.68  52574.55  51884.76  52158.79  42942.15  45718.65 
##       594       595       596       597       598       599       600       601 
##  40384.18  44599.11  53298.65  46678.39  52494.76  54855.26  60483.09  56669.18 
##       602       603       604       605       606       607       608       609 
##  61447.90  53077.89  54944.21  55714.90  58006.26  58576.13  58120.24  52340.03 
##       610       611       612       613       614       615       616       617 
##  59350.29  57424.50  54542.82  51272.89  44287.04  55713.53  59572.66  50574.06 
##       618       619       620       621       622       623       624       625 
##  60900.33  65054.80  58591.99  80657.69  65888.79  58273.98  60262.33  55649.48 
##       626       627       628       629       630       631       632       633 
##  46055.25  56684.66  37382.58  37243.83  46742.43  57148.74  55207.59  83727.30 
##       634       635       636       637       638       639       640       641 
##  73985.02  76169.72  77795.09  72559.85  65332.18  61991.01  49984.12  48364.33 
##       642       643       644       645       646       647       648       649 
##  47263.99  45757.30  44153.74  46811.07  51394.79  66256.48  80575.55  77719.89 
##       650       651       652       653       654       655       656       657 
##  78617.06  84445.70  97792.28  92589.13  63076.12  60546.52  57523.11  58500.56 
##       658       659       660       661       662       663       664       665 
##  54967.50  45450.19  47817.13  52103.41  51301.51  62778.92  62657.51  62944.92 
##       666       667       668       669       670       671       672       673 
##  51484.61  52834.21  53845.23  49218.02  43241.70  46254.45  43870.79  47332.12 
##       674       675       676       677       678       679       680       681 
##  45100.96  37990.86  32687.86  32685.38  35414.31  40111.76  42355.68  40374.77 
##       682       683       684       685       686       687       688       689 
##  40398.18  40843.67  35227.36  41510.54  41011.66  41308.73  43289.65  53919.19 
##       690       691       692       693       694       695       696       697 
##  62314.72  70305.15  59680.19  55717.13  52622.24  57738.67  48102.44  41847.51 
##       698       699       700       701       702       703       704       705 
##  35787.82  32530.89  31021.98  36488.39  34764.66  35432.39  41321.99  69772.07 
##       706       707       708       709       710       711       712       713 
##  75885.65  93304.56  89650.71  92227.02 107142.34 105991.46  83518.80  83863.57 
##       714       715       716       717       718       719       720       721 
##  75265.24  72389.14  70380.47  53236.46  48668.47  52135.00  49658.06  38852.85 
##       722       723       724       725       726       727       728       729 
##  44380.08  40678.66  42907.34  40798.08  31573.39  35495.20  36115.86  29798.00 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8276
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    7.231819  0.4886395    3.235115
## t2* 2190.687727 23.8431712  253.225333
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value  Upper CI
## 1 interrupt_var    3.050449       7.349241   13.6317
## 2    lag_depvar 1826.040110    2196.541303 2658.0561

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Jul 01 00:47:54 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Jul 01 00:48:02 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Jul 01 00:48:09 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Jul 01 00:48:16 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Jul 01 00:48:23 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Jul 01 00:48:30 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Jul 01 00:48:37 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Jul 01 00:48:44 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Jul 01 00:48:51 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Jul 01 00:48:58 2024
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_24 %>% 
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))
Item 2024 2023 2022 2021 2020
Agua 5.441667 5.195333 5.410333 5.849167 6.4696481
Comida 315.368000 366.009167 310.278417 317.896583 343.2892963
Comunicaciones 0.000000 0.000000 0.000000 0.000000 0.0000000
Electricidad 61.806667 38.104750 47.072333 29.523000 36.9351111
Enceres 45.813000 18.259750 20.086417 14.801167 25.5701852
Farmacia 0.000000 4.733250 1.831667 13.996083 7.6883889
Gas/Bencina 45.176833 35.219333 44.325000 13.583667 29.5499444
Diosi 49.019833 55.804250 31.180667 52.687833 44.7342222
donaciones/regalos 0.000000 0.000000 0.000000 14.340167 5.0873889
Electrodomésticos/ Mantención casa 10.000000 0.000000 3.944000 56.595000 16.4716296
VTR 21.991667 12.829167 25.156667 19.086917 19.3738519
Netflix 2.782833 4.555500 7.151583 7.028750 6.3054444
Otros 0.000000 0.000000 3.151083 0.000000 0.7002407
Total 557.400500 540.710500 499.588167 545.388333 542.1753519
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")

tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  47 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2382, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2024-07-09 00:04:58 sería de: 37.945 pesos// Percentil 95% más alto proyectado: 41.195,98

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 37608.45 37607.72
Lo.80 37614.16 37612.93
Point.Forecast 37945.40 38726.12
Hi.80 39776.26 43514.26
Hi.95 40780.94 46048.95


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(0,1,1) 
## 
## Coefficients:
##           ma1
##       -0.8210
## s.e.   0.0872
## 
## sigma^2 = 31404:  log likelihood = -415.62
## AIC=835.25   AICc=835.45   BIC=839.53
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(0,0,1) errors 
## 
## Coefficients:
##          ma1  intercept     xreg
##       0.2345   600.4144  13.4781
## s.e.  0.1340   241.9116   7.5769
## 
## sigma^2 = 29566:  log likelihood = -418.72
## AIC=845.45   AICc=846.13   BIC=854.08
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 803.9700 730.9179 781.5400
Lo.80 923.7849 851.1414 872.5926
Point.Forecast 1150.1207 1078.2489 1074.5299
Hi.80 1376.4566 1342.3602 1323.1873
Hi.95 1496.2715 1482.1722 1477.3248


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [4] Boom_0.9.15         scales_1.3.0        ggiraph_0.8.9      
##  [7] tidytext_0.4.1      DT_0.32             janitor_2.2.0      
## [10] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [13] xts_0.13.2          forecast_8.21.1     wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-11          
## [19] NLP_0.2-1           tsibble_1.1.4       lubridate_1.9.3    
## [22] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.2        
## [25] tidyr_1.3.1         tibble_3.2.1        tidyverse_2.0.0    
## [28] gsynth_1.2.1        lattice_0.20-45     GGally_2.2.1       
## [31] ggplot2_3.5.0       gridExtra_2.3       plotrix_3.8-4      
## [34] sparklyr_1.8.4      httr_1.4.7          readxl_1.4.3       
## [37] zoo_1.8-12          stringr_1.5.1       stringi_1.8.3      
## [40] data.table_1.15.0   reshape2_1.4.4      fUnitRoots_4021.80 
## [43] plyr_1.8.9          readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.2-0          systemfonts_1.0.5   selectr_0.4-2      
##   [4] lazyeval_0.2.2      websocket_1.4.1     crosstalk_1.2.1    
##   [7] listenv_0.9.1       digest_0.6.34       foreach_1.5.2      
##  [10] htmltools_0.5.7     fansi_1.0.6         ggfortify_0.4.16   
##  [13] magrittr_2.0.3      doParallel_1.0.17   tzdb_0.4.0         
##  [16] globals_0.16.2      vroom_1.6.5         sandwich_3.1-0     
##  [19] askpass_1.2.0       timechange_0.3.0    anytime_0.3.9      
##  [22] tseries_0.10-55     colorspace_2.1-0    xfun_0.42          
##  [25] crayon_1.5.2        jsonlite_1.8.8      iterators_1.0.14   
##  [28] glue_1.7.0          gtable_0.3.4        car_3.1-2          
##  [31] quantmod_0.4.26     abind_1.4-5         mvtnorm_1.2-4      
##  [34] DBI_1.2.2           rngtools_1.5.2      Rcpp_1.0.12        
##  [37] lfe_2.9-0           viridisLite_0.4.2   xtable_1.8-4       
##  [40] bit_4.0.5           Formula_1.2-5       htmlwidgets_1.6.4  
##  [43] timeSeries_4032.109 gplots_3.1.3.1      ellipsis_0.3.2     
##  [46] spatial_7.3-14      farver_2.1.1        pkgconfig_2.0.3    
##  [49] nnet_7.3-16         sass_0.4.8          dbplyr_2.4.0       
##  [52] chromote_0.2.0      utf8_1.2.4          labeling_0.4.3     
##  [55] tidyselect_1.2.0    rlang_1.1.3         later_1.3.2        
##  [58] munsell_0.5.0       cellranger_1.1.0    tools_4.1.2        
##  [61] cachem_1.0.8        cli_3.6.2           generics_0.1.3     
##  [64] evaluate_0.23       fastmap_1.1.1       yaml_2.3.8         
##  [67] processx_3.8.3      knitr_1.45          bit64_4.0.5        
##  [70] caTools_1.18.2      future_1.33.1       nlme_3.1-153       
##  [73] doRNG_1.8.6         slam_0.1-50         xml2_1.3.6         
##  [76] tokenizers_0.3.0    compiler_4.1.2      rstudioapi_0.15.0  
##  [79] curl_5.2.0          bslib_0.6.1         highr_0.10         
##  [82] ps_1.7.6            fBasics_4032.96     Matrix_1.6-5       
##  [85] its.analysis_1.6.0  urca_1.3-3          vctrs_0.6.5        
##  [88] pillar_1.9.0        lifecycle_1.0.4     lmtest_0.9-40      
##  [91] jquerylib_0.1.4     bitops_1.0-7        R6_2.5.1           
##  [94] promises_1.2.1      KernSmooth_2.23-20  janeaustenr_1.0.0  
##  [97] parallelly_1.37.0   codetools_0.2-18    ggstats_0.5.1      
## [100] assertthat_0.2.1    boot_1.3-28         gtools_3.9.5       
## [103] MASS_7.3-54         openssl_2.1.1       withr_3.0.0        
## [106] fracdiff_1.5-3      parallel_4.1.2      hms_1.1.3          
## [109] quadprog_1.5-8      timeDate_4032.109   rmarkdown_2.25     
## [112] snakecase_0.11.1    carData_3.0-5       TTR_0.24.4
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))